System and method of dynamic corrective enzyme selection and formulation for pulp and paper production

ABSTRACT

Systems and methods as disclosed herein automatically provide real-time dosing corrections for an industrial process wherein enzyme blends are applied to natural fibers for pulp/ paper production. An initial enzyme blend (e.g., enzymes and supporting formulation components, as relevant) and respective dose rates are selected to be applied based on expected fiber surface substrate characterization, expected fiber quality characterization, the physical conditions of the system being treated, respective characteristics of the initially selected enzyme blend components, etc. Upon application of the initial enzyme blend, online sensors provide real-time feedback data corresponding to measured actual values for the fiber surface substrate characterization and fiber quality characterization. A replacement enzyme blend (enzymes and supporting formulation components) and respective dose rates thereof is dynamically selected based on the feedback data. The enzyme dosing stage can be optimized responsive to product changes and/or variations in fiber sources/blend and/or physical conditions, substantially in real time.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Patent ApplicationNo. 63/125,250, filed Dec. 14, 2020, and which is hereby incorporated byreference.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the reproduction of the patent document or the patentdisclosure, as it appears in the U.S. Patent and Trademark Office patentfile or records, but otherwise reserves all copyright rights whatsoever.

FIELD OF THE INVENTION

The present invention relates generally to systems and methods forcomponent characterization and feedback implementation in industrialprocesses.

More particularly, embodiments of inventions as disclosed herein relateto systems and methods for optimizing enzyme selection and dosing inpulp and paper production, and further proactively correcting enzymedosing in real time based on feedback from online sensors and dataanalytics. However, alternative embodiments of the systems and methodsas disclosed herein may be directed to other processes such as forexample biomass production.

BACKGROUND

Conventional paper making processes may generally include: the formationan aqueous suspension of cellulosic fibers, commonly known as pulp;adding various processing and paper enhancing materials, such asstrengthening, retention, drainage aid, and/or sizing materials, orother functional additives; sheeting and drying the fibers to form adesired cellulosic web; and post-treating the web to provide variousdesired characteristics to the resulting paper, such as surfaceapplication of sizing materials, and the like. Various types ofenzymatic compositions, of various enzyme dose ratios, may accordinglybe applied to treat the fibers to improve properties of the pulp (e.g.,improve the drainage of the fiber suspension slurry) and/or theproperties of the finished sheet (e.g. strength, porosity, softness).

As pulp and paper producers purchase and/or produce fiber and undertakegrade development activities, the fiber properties of the fiber areobserved to change as a result of multiple reasons including, but notlimited to, the species of tree used in generating the fibers, the blendof fiber used, whether a fiber is virgin or recycled, tree growthconditions, seasonality, pulping process, pulp treatment, and the like.This introduces inherent variability into their process and can alterthe type and amount of enzyme that should be dosed to a system for theapplication of bleaching and/or fiber modification enzymes. Adding thewrong mix or dose of enzymes may result in unnecessary activities, wastein chemical spend, or over-development of the fiber, further resultingin lost efficiency.

It would be desirable to generate and utilize a database of fibersurface characterization data, fiber quality analysis, system physicalconditions (e.g. temp, pH, flow rate, conductivity, ORP, biocideresidual) and appropriate enzyme pumping equipment to ensure that thedosing of an enzyme is optimized for a particular fiber/furnish andsystem.

It would further be desirable if such an optimized dosing techniquecould be applied to the bleaching process and/or on paper machines toassist with bleaching and/or physical properties of the finished sheet(e.g., tensile strength, burst, drainage, porosity, etc.).

Conventional systems and methods are known to implement limited examplesof fiber surface characterization, fiber quality analysis, and manualand online sensors for physical conditions and enzyme formulation forimproved stability and performance.

However, such conventional techniques are substantially limited in thatthey utilize technology that is all too frequently unreliable inpractice, and typically focus on a single piece of the enzyme selectionand formulation process, rather than providing or otherwise enabling theimplementation of a more holistic framework.

BRIEF SUMMARY

Generally stated, systems and methods as disclosed herein representtechnical advancements over the prior art, at least in that they mayutilize a database of information to provide algorithms for productselection and application that can be adjusted, substantially inreal-time, using measurements of fiber and physical conditions. Suchalgorithms may be dynamic in nature based on observed correlations overtime between various combinations of process inputs and desired outcomesin the form of fiber quality, product efficacy, and the like.

Systems as disclosed herein may preferably implement accessiblevisualization graphics, alarms, notifications, and the like via onboarduser interfaces, mobile computing devices, web-based interfaces, etc.,to supplement any automated capabilities with actionable insightsrelating to the associated processes.

Exemplary techniques for predictive model development may includesupervised and unsupervised learning, hard and soft clustering,classification, forecasting, and the like.

One objective of the present disclosure is to provide a database ofseveral key fiber, enzyme, and system data points to identify an optimumenzyme blend and dose for a particular application. In short, a systemand method may relate fiber surface substrate characterization, fiberquality analysis data (including elements like fiber length, fiberwidth, fibrillation, kink, curl, etc.), enzyme activity fingerprints,physical measurements from the process (pH, temperature, and flowrate/retention time), and product efficacy data to provide an initialproduct blend and dose rate. The system can be implemented for anindividual aspect of the overall process or may be implemented as partof a pumping skid that can blend multiple raw materials together toattain the optimum blend and dose rate. This skid may for example beintegrated with online sensors of flow rate, temperature, chemicalresidual and pH, as well as system data relating to the strength,freeness and quality of the finished sheet, to determine if optimumdosing has been achieved. Furthermore, as data related to fiber qualityand substrate prevalence is collected and uploaded to the system, thebalance of the enzymatic raw materials present may be adjusted overtime.

System outputs may for example feed into a dosing skid that would blendenzymatic raw materials for delivery straight into a pulp or papermakingapplication and may be particularly advantageous with respect to atleast pulp bleaching and tissue/packaging/paper making applications.

The systems and methods as disclosed herein may further utilize afront-end data capture application that feeds information into theoverall database, wherein such information may further be communicatedto the blending and dosing skid either wirelessly or via integratedsignals. Various sensors, controllers, online devices, and otherintermediate components may be “Internet-of-things” (IoT) compatible, orotherwise comprise an interrelated network, wherein relevant outputs maybe uploaded to a cloud-based server in real time.

In view of some or all of the aforementioned issues and objectives, afirst exemplary embodiment of a method as disclosed herein automaticallyprovides real-time dosing corrections in an industrial process whereinone or more enzymes (and supporting formulation components) are appliedto natural fibers for producing a pulp or paper product. Such naturalfibers may of course include wood fibers, but also potentially othercellulosic fibers and non-traditional paper furnishes including bamboo,grasses (e.g. bagasse) etc. A first step includes selecting an initialenzyme blend to be applied, and respective dose rates for one or morecomponents thereof, based at least in part on input data comprising anexpected fiber surface substrate characterization for the pulp or paperproduct, an expected fiber quality characterization for the pulp orpaper product, and one or more respective characteristics of the one ormore enzyme blend components. Upon application of the initial enzymeblend and respective dose rates for the one or more components thereof,real-time feedback data is provided corresponding to measured actualvalues for the fiber surface substrate characterization and fiberquality characterization. Another step includes dynamically selecting areplacement enzyme blend to be applied, and respective dose rates forone or more components thereof, based at least in part on the feedbackdata.

In a second embodiment, one exemplary aspect according to theabove-referenced first embodiment may include that the initial enzymeblend to be applied and the respective dose rates are selected furtherbased on expected values for one or more industrial processcharacteristics, and the real-time feedback data further comprisesmeasured values for the one or more industrial process characteristics.

In a third embodiment, one exemplary aspect according to any one of theabove-referenced first or second embodiments may further include thatthe real-time feedback data further comprises measured values forindustrial process characteristics comprising one or more of atemperature, a system flow rate, a pH value, a conductivity value, anORP value, a biocide residual value, and a residence time. Still furtherexamples of characteristics considered may include pulp furnish andpulping method.

In a fourth embodiment, one exemplary aspect according to any one of theabove-referenced first to third embodiments may include that the initialenzyme bland and respective dose rates are selected using apredetermined model associated with a pulp or paper product to resultfrom the industrial process, and the method further comprisesselectively altering the predetermined model based at least in part onthe provided real-time feedback data.

In a fifth embodiment, one exemplary aspect according to any one of theabove-referenced first to fourth embodiments may include blending theone or more components of the initial enzyme blend in accordance with afirst overall dose rate, and applying said blended one or morecomponents of the initial enzyme blend in the industrial process. Saidexemplary aspect may be provided via a dosing control stage (e.g.,embodied by or otherwise including a dosing controller), which mayfurther be configured for example for blending the one or morecomponents of the selected replacement enzyme blend in accordance withan overall dose rate, and applying said blended one or more componentsof the selected replacement enzyme blend in place of one or morecomponents of the initial enzyme blend.

In a sixth embodiment, one exemplary aspect according to any one of theabove-referenced first to fifth embodiments may include that the fiberquality characterization is determined with respect to one or more of afiber length, width, fibrillation, cell wall thickness, finesdensity/distribution, fiber kink, and fiber curl.

In a seventh embodiment, one exemplary aspect according to any one ofthe above-referenced first to sixth embodiments may include that thereal-time feedback data further comprises system performance dataregarding one or more of a fiber strength, a porosity, a caliper, asoftness, a crepe count, a freeness and a drainage of the pulp or paperproduct.

In an eighth embodiment, one exemplary aspect according to any one ofthe above-referenced first to seventh embodiments may include that theselected initial enzyme blend and the dynamically selected replacementenzyme blend to be applied, and respective dose rates thereof, areprovided to a pulp bleaching process controller.

In a ninth embodiment, one exemplary aspect according to any one of theabove-referenced first to seventh embodiments may include that theselected initial enzyme blend and the dynamically selected replacementenzyme blend to be applied, and respective dose rates thereof, areprovided to a paper manufacturing controller.

In a tenth exemplary embodiment, a system as disclosed hereinautomatically provides real-time dosing corrections in an industrialprocess wherein one or more components of an enzyme blend are applied tonatural fibers for producing a pulp or paper product. A data storageunit comprises models correlating one or more pulp or paper productswith respective expected fiber surface substrate characterization andexpected fiber quality characterization, and further comprising datacorresponding to enzyme characteristics. One or more online sensors areconfigured to generate output signals representative of measured actualvalues for the fiber surface substrate characterization and fiberquality characterization. A production stage may include a plurality ofcontainers each configured to store and selectively deliver respectiveraw materials corresponding to selected enzyme blend components. Adosing control stage may including one or more computing devicesfunctionally linked to the data storage unit and to the one or moreonline sensors and configured to direct the performance of steps in amethod corresponding to any one of the first to ninth embodiments.

The one or more computing devices may for example include a dosingcontroller according to the above-referenced fifth exemplary embodiment.

In one further optional aspect, the production stage according to thetenth exemplary embodiment may comprise a pulp bleaching processcontroller configured to receive and apply the selected initial set ofone or more enzymes and the dynamically selected replacement set of oneor more enzymes, and respective dose rates thereof.

In one further optional aspect, the production stage according to thetenth exemplary embodiment may comprise a paper manufacturing controllerconfigured to receive and apply the selected initial set of one or moreenzymes and the dynamically selected replacement set of one or moreenzymes, and respective dose rates thereof.

The computing device, the dosing controller, the pulp bleaching processcontroller, and/or paper manufacturing controller as described withrespect to any one of the first to tenth exemplary embodiments maywithin the scope of the present disclosure be integrated in the samedevice, or some or all of them may be provided as discrete components.

Numerous objects, features and advantages of the embodiments set forthherein will be readily apparent to those skilled in the art upon readingof the following disclosure when taken in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram representing an exemplary embodiment of asystem as disclosed herein.

FIG. 2 is a diagram and simplified flowchart representing an exemplaryembodiment of a method as disclosed herein for initial product and doseselection and subsequent adjustment based upon fiber and systemparameters.

DETAILED DESCRIPTION

Briefly stated, systems and methods as disclosed herein may beimplemented to correlate furnish, system, and enzyme properties toprovide a tailored or otherwise optimized dosing regimen for an enzymeblend as needed to maintain the ideal properties of a finished pulp orpaper product. While the following description of embodiments of asystem and method as disclosed herein may focus for illustrativepurposes on the selection and formulation of one or more enzymes, one ofskill in the art may appreciate the relevance of such methods in thecorresponding selection and/or formulation of supporting components forenzymatic technologies such as non-ionic surfactants, polymers, etc., aspotentially contributing to optimization of the system with respect toenzymatic activity. An enzyme and corresponding aids such as polymericsurfactants as used in systems and methods as disclosed herein mayaccordingly be supplied separately or collectively as an enzyme blend,the selection, formulation, and dynamic adaptation of which may beenhanced by various embodiments of the present disclosure.

Referring initially to FIG. 1, a system 100 as disclosed herein maycomprise multiple dosing control stages 106 a, 106 b, . . . 106 x asillustrated in functional coordination with a production stage 110,e.g., a pulp or paper production stage, wherein each dosing controlstage may be provided for respective enzymes to be applied in a preparedcomposition. Alternatively, selection and dosing operations may beperformed with respect to a plurality of enzymes mixed together to forman enzyme product by a single dosing control stage within the scope ofthe present disclosure.

An array of sensors 102, for example including online sensors 102, arelinked to the dosing control stage 106 along with data storage 104, forexample including one or more databases 104 with models, algorithms, anddata for implementing the methods and operations as disclosed herein.Outputs from the dosing control stage may include dosing information 108provided to a pulp or paper production stage 110, which further providesfeedback information 112 to the dosing control stage. The feedback 112from the production stage 110 is illustrated independently with respectto the array of sensors 102, but it may be understood that the feedback112 may include signals from the array of sensors. The dosing controlstage 106 may further provide feedback information 114 to the datastorage 104, for example in the context of model improvement viaobservation and machine learning.

The term “sensors” may include, without limitation, physical levelsensors, relays, and equivalent monitoring devices as may be provided todirectly measure values or variables for associated process componentsor elements, or to measure appropriate derivative values from which theprocess components or elements may be measured or calculated. The term“online” as used herein may generally refer to the use of a device,sensor, or corresponding elements proximally located to a container,machine or associated process elements, and generating output signalssubstantially in real time corresponding to the desired processelements, as distinguished from manual or automated sample collectionand “offline” analysis in a laboratory or through visual observation byone or more operators.

Online sensors 102 are well known in the art for the purpose of sensingor calculating characteristics such as temperature, flow rate, ORP,conductivity, biocide residual, pH and the like, and exemplary suchsensors are considered as being fully compatible with the scope of asystem and method as disclosed herein. Individual sensors may beseparately mounted and configured, or the system 100 may provide amodular housing which includes, e.g., a plurality of sensors or sensingelements. Sensors or sensor elements may be mounted permanently orportably in a particular location respective to the production stage 110or may be dynamically adjustable in position so as to collect data froma plurality of locations during operation.

Online sensors 102 as disclosed herein may provide substantiallycontinuous measurements with respect to various process components andelements, and substantially in real-time. The terms “continuous” and“real-time” as used herein, at least with respect to the disclosedsensor outputs, does not require an explicit degree of continuity, butrather may generally describe a series of measurements corresponding tophysical and technological capabilities of the sensors, the physical andtechnological capabilities of the transmission media, the physical andtechnological capabilities of any intervening local controller,communications device, and/or interface configured to receive the sensoroutput signals, etc. For example, measurements may be taken and providedperiodically and at a rate slower than the maximum possible rate basedon the relevant hardware components, or based on a communicationsnetwork configuration which smooths out input values over time, andstill be considered “continuous.”

A user interface (not shown) may further enable users such as operators,administrators, and the like to provide periodic input with respect toconditions or states of additional components of relevance to models,algorithms, or the like as further discussed herein. The user interfacemay be in functional communication with the dosing control stage 106, adistributed control system (not shown) associated with the industrialfacility, and/or a remote hosted server (not shown) to receive anddisplay process-related information, or to provide other forms offeedback with respect to, e.g., control processes as further discussedherein. The term “user interface” as used herein may unless otherwisestated include any input-output module with respect to the controllerand/or the hosted data server including but not limited to: a stationaryoperator panel with keyed data entry, touch screen, buttons, dials orthe like; web portals, such as individual web pages or thosecollectively defining a hosted website; mobile device applications, andthe like.

The term “communications network” as used herein with respect to datacommunication between two or more system components or otherwise betweencommunications network interfaces associated with two or more systemcomponents may refer to any one of, or a combination of any two or moreof, telecommunications networks (whether wired, wireless, cellular orthe like), a global network such as the Internet, local networks,network links, Internet Service Providers (ISP's), and intermediatecommunication interfaces. Any one or more conventionally recognizedinterface standards may be implemented therewith, including but notlimited to Bluetooth, RF, Ethernet, and the like.

An embodiment of a method 200 may now be described with reference toFIG. 2, the illustrated steps in which are merely exemplary and notintended as expressly limiting the scope of the present disclosureunless otherwise specifically stated.

Various inputs 211-215 are provided for initial product selection 220,which may refer to selection of each enzyme to be applied, to one of aplurality of enzymes to be applied, or for example to an enzyme blendfurther incorporating one or more aids such as non-ionic surfactants orthe like.

Fiber surface substrate characterization data 211 may in variousembodiments be selectively extracted from a database communicativelylinked to the dosing controller. Numerous conventional techniques areknown for characterizing the fiber surface substrate in a manner thataids enzyme selection and formulation, and such techniques may beconsidered within the scope of the present disclosure and in combinationwith one or more other inputs as further described herein. Numeroustechniques are conventionally known for fiber surface characterization,including for example X-ray photoelectron spectroscopy (XPS), scanningelectron microscopy (SEM), time-of-flight secondary ion massspectrometry (ToF-SIMS), Fourier transform infra-red (FTIR), etc.However, in the context of the present disclosure it may be preferred toutilize techniques for rapid characterization of fiber surface polymersthat would better enable the prediction of the impact of varioustreatments on pulp or paper. An exemplary sensor (detection probe) andmethods of use thereof as disclosed in U.S. Pat. No. 10,788,477 isincorporated herein by reference and may accordingly be implemented forsuch characterization within the scope of the present disclosure, orotherwise data obtained therefrom may be selectively accessible in adatabase for various enzyme selection and formulation steps oroperations in accordance with the other inputs as described below.

Fiber quality data 212 may be collected and transmitted or uploaded tothe dosing controller from one or more sensors as are known in the art,substantially in real time, and relating for example to conventionalfiber quality variables such as fiber length, fiber width, fibercoarseness, fiber kink angle, fines quantity/density, fiber curl,external fibrillation, cell wall thickness, and the like. Such sensorsmay within the scope of the present disclosure be online measuringdevices and/or automated or manually operable offline fiber imageanalyzers, and the like. Relevant outputs to the dosing control stagemay further include raw sense signals, converted and/or derivativevalues thereof, machine learning classifications of sense signals, andthe like.

Enzyme function characterization data 213 may for example relate toactivity profiles or fingerprints as measured or otherwise retrievedfrom data storage in association with the given enzyme.

Application outcomes data 214 may generally relate to observed resultsfrom system performance feedback, for example in the context of machinelearning with an objective to optimize future product blends andrelative dosing, but may also encompass user inputs from a userinterface to for example further define, confirm, or otherwise reversesystem-generated findings.

Physical conditions data 215 may be collected and transmitted oruploaded to the dosing controller in substantially real time from one ormore sensors as are known in the art, relating for example toconventional variables such as temperatures, pH values, flow rates,residence times, and the like. Such sensors to provide physicalconditions data may within the scope of the present disclosure be onlinesensors and/or manual sensors.

A product identification and initial dose rate setting stage 230 maygenerally be configured to utilize the aforementioned inputs, e.g.,relevant fiber, enzyme, and system data points, to identify an optimumenzyme blend and initial dose rate for a selected product application.

The next stage 240 and associated sub-steps collectively refer toapplication of selected enzyme(s) on the process, with a newly specifieddose rate 250 and product blend 260. Feedback of measured physicalconditions from the process, including for example a measured retentiontime or system flow rate 251, a measured pH 252, a measured temperature253, or the like. Additional data influencing the product blend mayfurther include new fiber surface substrate characterization data 261,new fiber quality analysis data 262, enzyme characterization data 263.

The newly specified product and an associated ongoing dose rate 270 maybe provided, along with any other information as may be determinedrelevant by the dosing controller, to an onsite blending and pumpingcontroller and associated equipment 280. In an embodiment as previouslynoted herein, the dosing control stage 106 (or respective dosing controlstages 106 a, 106 b for different enzymes) may be integrated with theproduction stage 110, for example in the context of a dosing skid withappropriate enzyme pumping equipment. In other embodiments, the dosingcontrol stage 106 (or respective dosing control stages 106 a, 106 b fordifferent enzymes) may be discrete products or components of the overallsystem and configured to transmit the relevant information fordownstream implementation (i.e., enzyme formulation and pumping) via acommunications network, e.g., either wirelessly or via integratedsignals.

Feedback data comprising system performance data 290 may pertain tobleaching and/or physical qualities of the finished product (e.g.,sheet) including for example strength data, porosity, caliper, crepecount, softness, freeness, drainage, and the like, as preferablyobtained in real time or a reasonable approximation thereof. Suchfeedback may be provided to the dosing controller to determine ifoptimum dosing has been achieved, and subsequently to repeat steps 240to 280 as needed to dynamically adjust the selection and/or balance ofthe enzymatic raw materials present over time. The system performancedata may be obtained from offline or online methods, and may beaccessible directly from existing process data repositories, e.g.distributed control systems (DCS)

Throughout the specification and claims, the following terms take atleast the meanings explicitly associated herein, unless the contextdictates otherwise. The meanings identified below do not necessarilylimit the terms, but merely provide illustrative examples for the terms.The meaning of “a,” “an,” and “the” may include plural references, andthe meaning of “in” may include “in” and “on.” The phrase “in oneembodiment,” as used herein does not necessarily refer to the sameembodiment, although it may. As used herein, the phrase “one or moreof,” when used with a list of items, means that different combinationsof one or more of the items may be used and only one of each item in thelist may be needed. For example, “one or more of” item A, item B, anditem C may include, for example, without limitation, item A or item Aand item B. This example also may include item A, item B, and item C, oritem Band item C.

The various illustrative logical blocks, modules, and algorithm stepsdescribed in connection with the embodiments disclosed herein can beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. The described functionality can be implemented invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the disclosure.

The various illustrative logical blocks and modules described inconnection with the embodiments disclosed herein can be implemented orperformed by a machine, such as a general purpose processor, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general-purpose processor can be a microprocessor,but in the alternative, the processor can be a controller,microcontroller, or state machine, combinations of the same, or thelike. A processor can also be implemented as a combination of computingdevices, e.g., a combination of a DSP and a microprocessor, a pluralityof microprocessors, one or more microprocessors in conjunction with aDSP core, or any other such configuration.

The steps of a method, process, or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module can reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of computer-readablemedium known in the art. An exemplary computer-readable medium can becoupled to the processor such that the processor can read informationfrom, and write information to, the memory/ storage medium. In thealternative, the medium can be integral to the processor. The processorand the medium can reside in an ASIC. The ASIC can reside in a userterminal. In the alternative, the processor and the medium can reside asdiscrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “might,”“may,” “e.g.,” and the like, unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments include, while other embodiments donot include, certain features, elements and/or states. Thus, suchconditional language is not generally intended to imply that features,elements and/or states are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without author input or prompting, whether thesefeatures, elements and/or states are included or are to be performed inany particular embodiment.

The previous detailed description has been provided for the purposes ofillustration and description. Thus, although there have been describedparticular embodiments of a new and useful invention, it is not intendedthat such references be construed as limitations upon the scope of thisinvention except as set forth in the following claims.

What is claimed is:
 1. A method of automatically providing real-timedosing corrections for an industrial process wherein one or moreenzymatic compositions are applied to natural fibers for producing apulp or paper product, the method comprising: selecting an initialenzyme blend to be applied, and respective dose rates for one or morecomponents thereof, based at least in part on input data comprising anexpected fiber surface substrate characterization for the pulp or paperproduct, an expected fiber quality characterization for the pulp orpaper product, and one or more respective characteristics of the one ormore enzyme blend components; upon application of the initial enzymeblend and respective dose rates for the one or more components thereof,providing real-time feedback data corresponding to measured actualvalues for the fiber surface substrate characterization and fiberquality characterization; and dynamically selecting a replacement enzymeblend to be applied, and respective dose rates for one or morecomponents thereof, based at least in part on the feedback data.
 2. Themethod of claim 1, wherein the initial enzyme blend to be applied andthe respective dose rates are selected further based on expected valuesfor one or more industrial process characteristics, and the real-timefeedback data further comprises measured values for the one or moreindustrial process characteristics.
 3. The method of claim 2, whereinthe real-time feedback data further comprises measured values forindustrial process characteristics comprising one or more of atemperature, a system flow rate, a pH value, a conductivity value, anORP value, a biocide residual value, and a residence time.
 4. The methodof claim 1, wherein the initial enzyme bland and respective dose ratesare selected using a predetermined model associated with a pulp or paperproduct to result from the industrial process, and the method furthercomprises selectively altering the predetermined model based at least inpart on the provided real-time feedback data.
 5. The method of claim 1,further comprising: blending the one or more components of the initialenzyme blend in accordance with a first overall dose rate, and applyingsaid blended one or more components of the initial enzyme blend in theindustrial process.
 6. The method of claim 5, further comprising:blending the one or more components of the selected replacement enzymeblend in accordance with an overall dose rate, and applying said blendedone or more components of the selected replacement enzyme blend in placeof one or more components of the initial enzyme blend.
 7. The method ofclaim 1, wherein the fiber quality characterization is determined withrespect to one or more of a fiber length, width, fibrillation, cell wallthickness, fines density/distribution, fiber kink, and fiber curl. 8.The method of claim 1, wherein the real-time feedback data furthercomprises system performance data regarding one or more of a fiberstrength, a porosity, a caliper, a softness, a crepe count, a freenessand a drainage of the pulp or paper product.
 9. The method of claim 1,wherein the selected initial enzyme blend and the dynamically selectedreplacement enzyme blend to be applied, and respective dose ratesthereof, are provided to a pulp bleaching process controller.
 10. Themethod of claim 1, wherein the selected initial enzyme blend and thedynamically selected replacement enzyme blend to be applied, andrespective dose rates thereof, are provided to a paper manufacturingcontroller.
 11. A system for automatically providing real-time dosingcorrections in an industrial process wherein one or more components ofan enzyme blend are applied to natural fibers for producing a pulp orpaper product, the system comprising: a data storage unit comprisingmodels correlating one or more pulp or paper products with respectiveexpected fiber surface substrate characterization and expected fiberquality characterization, and further comprising data corresponding toenzyme characteristics; one or more online sensors configured togenerate output signals representative of measured actual values for thefiber surface substrate characterization and fiber qualitycharacterization; a production stage comprising a plurality ofcontainers each configured to store and selectively deliver respectiveraw materials corresponding to selected enzyme blend components; and adosing control stage comprising one or more computing devicesfunctionally linked to the data storage unit and to the one or moreonline sensors and configured to select an initial enzyme blend to beapplied, and respective dose rates for one or more components thereof,based at least in part on input data comprising an expected fibersurface substrate characterization for the pulp or paper product beingproduced, an expected fiber quality characterization for the pulp orpaper product being produced, and one or more respective characteristicsof the one or more enzyme blend components, upon application of theinitial enzyme blend, provide real-time feedback data from the one ormore sensors corresponding to measured actual values for the fibersurface substrate characterization and fiber quality characterization,and dynamically select a replacement enzyme blend to be applied, andrespective dose rates for one or more components thereof, based at leastin part on the feedback data.
 12. The system of claim 11, wherein theinitial enzyme blend and the respective dose rates for one or morecomponents thereof are selected further based on expected values for oneor more industrial process characteristics, and the real-time feedbackdata further comprises measured values for the one or more industrialprocess characteristics.
 13. The system of claim 12, wherein thereal-time feedback data further comprises measured values by the one ormore online sensors corresponding to industrial process characteristicscomprising one or more of a temperature value, a system flow rate, aconductivity value, an ORP value, a biocide residual value (e.g. freehalogen), a pH value, and a residence time.
 14. The system of claim 11,wherein the initial enzyme blend and the respective dose rates for oneor more components thereof are selected using a predetermined modelassociated with a pulp or paper product to result from the industrialprocess, and the computing device is further configured to selectivelyalter the predetermined model based at least in part on the providedreal-time feedback data.
 15. The system of claim 11, wherein the dosingcontrol stage is configured to: blend the one or more components of theinitial enzyme blend in accordance with a first overall dose rate, andapply said blended initial enzyme blend in the industrial process. 16.The system of claim 15, wherein the dosing control stage is furtherconfigured to: blend the one or more components of the selectedreplacement enzyme blend in accordance with an overall dose rate, andapply said blended replacement enzyme blend in place of the initialenzyme blend.
 17. The system of claim 11, wherein the fiber qualitycharacterization is determined with respect to one or more of a fiberlength, width, fibrillation, cell wall thickness, finesdensity/distribution, fiber kink, and fiber curl.
 18. The system ofclaim 11, wherein the real-time feedback data further comprises systemperformance data regarding one or more of a fiber strength, a porosity,a caliper, a softness, a crepe count, a freeness and a drainage of thepulp or paper product.
 19. The system of claim 11, the production stagefurther comprising a pulp bleaching process controller configured toreceive and apply the selected initial set of one or more enzymes andthe dynamically selected replacement set of one or more enzymes, andrespective dose rates thereof.
 20. The system of claim 11, theproduction stage further comprising a paper manufacturing controllerconfigured to receive and apply the selected initial set of one or moreenzymes and the dynamically selected replacement set of one or moreenzymes, and respective dose rates thereof.